






'''simple docstring'''
import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )






class   __SCREAMING_SNAKE_CASE   (      unittest.TestCase					):




 def __init__(   self						:				Dict       ,   __lowercase						:				List[str]       ,   __lowercase						:				Union[str, Any]=13       ,   __lowercase						:				List[str]=7       ,   __lowercase						:				Dict=True       ,   __lowercase						:				Any=True       ,   __lowercase						:				Dict=True       ,   __lowercase						:				Optional[Any]=True       ,   __lowercase						:				int=99       ,   __lowercase						:				Any=32       ,   __lowercase						:				str=5       ,   __lowercase						:				List[str]=4       ,   __lowercase						:				Optional[Any]=37       ,   __lowercase						:				List[str]="gelu"       ,   __lowercase						:				str=0.1       ,   __lowercase						:				Tuple=0.1       ,   __lowercase						:				Optional[Any]=5_12       ,   __lowercase						:				Any=16       ,   __lowercase						:				Optional[int]=2       ,   __lowercase						:				List[str]=0.02       ,   __lowercase						:				Optional[Any]=4       ,   )						->    Tuple:
       SCREAMING_SNAKE_CASE__  :					int						=parent
       SCREAMING_SNAKE_CASE__  :					str						=batch_size
       SCREAMING_SNAKE_CASE__  :					str						=seq_length
       SCREAMING_SNAKE_CASE__  :					List[Any]						=is_training
       SCREAMING_SNAKE_CASE__  :					Any						=use_attention_mask
       SCREAMING_SNAKE_CASE__  :					Dict						=use_token_type_ids
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=use_labels
       SCREAMING_SNAKE_CASE__  :					Tuple						=vocab_size
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=hidden_size
       SCREAMING_SNAKE_CASE__  :					int						=num_hidden_layers
       SCREAMING_SNAKE_CASE__  :					int						=num_attention_heads
       SCREAMING_SNAKE_CASE__  :					List[Any]						=intermediate_size
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=hidden_act
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__  :					int						=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=max_position_embeddings
       SCREAMING_SNAKE_CASE__  :					Dict						=type_vocab_size
       SCREAMING_SNAKE_CASE__  :					List[Any]						=type_sequence_label_size
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=initializer_range
       SCREAMING_SNAKE_CASE__  :					str						=num_choices




 def   __magic_name__						(   self						:				Tuple   )						->    List[str]:
       SCREAMING_SNAKE_CASE__  :					Any						=ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size   )

       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=None
       if self.use_attention_mask:
             SCREAMING_SNAKE_CASE__  :					List[Any]						=random_attention_mask([self.batch_size, self.seq_length]   )

       SCREAMING_SNAKE_CASE__  :					Any						=None
       if self.use_token_type_ids:
             SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=ids_tensor([self.batch_size, self.seq_length]       ,   self.type_vocab_size   )

       SCREAMING_SNAKE_CASE__  :					List[Any]						=AlbertConfig(
           vocab_size=self.vocab_size       ,   hidden_size=self.hidden_size       ,   num_hidden_layers=self.num_hidden_layers       ,   num_attention_heads=self.num_attention_heads       ,   intermediate_size=self.intermediate_size       ,   hidden_act=self.hidden_act       ,   hidden_dropout_prob=self.hidden_dropout_prob       ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,   max_position_embeddings=self.max_position_embeddings       ,   type_vocab_size=self.type_vocab_size       ,   is_decoder=UpperCamelCase__       ,   initializer_range=self.initializer_range       ,   )

       return config, input_ids, token_type_ids, attention_mask




 def   __magic_name__						(   self						:				Optional[Any]   )						->    Dict:
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__  :					str						=config_and_inputs
       SCREAMING_SNAKE_CASE__  :					Tuple						={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict






@require_flax
class   __SCREAMING_SNAKE_CASE   (      _A						,					unittest.TestCase					):
 snake_case_					=				(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )




 def   __magic_name__						(   self						:				Union[str, Any]   )						->    Any:
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=FlaxAlbertModelTester(self   )




 @slow
 def   __magic_name__						(   self						:				Optional[Any]   )						->    List[Any]:






       for model_class_name in self.all_model_classes:
             SCREAMING_SNAKE_CASE__  :					Dict						=model_class_name.from_pretrained('''albert-base-v2'''   )
             SCREAMING_SNAKE_CASE__  :					List[Any]						=model(np.ones((1, 1)   )   )
             self.assertIsNotNone(UpperCamelCase__   )






@require_flax
class   __SCREAMING_SNAKE_CASE   (      unittest.TestCase					):




 @slow
 def   __magic_name__						(   self						:				int   )						->    Dict:
       SCREAMING_SNAKE_CASE__  :					Dict						=FlaxAlbertModel.from_pretrained('''albert-base-v2'''   )
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]   )
       SCREAMING_SNAKE_CASE__  :					Any						=np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
       SCREAMING_SNAKE_CASE__  :					List[str]						=model(UpperCamelCase__       ,   attention_mask=UpperCamelCase__   )[0]
       SCREAMING_SNAKE_CASE__  :					List[str]						=(1, 11, 7_68)
       self.assertEqual(output.shape       ,   UpperCamelCase__   )
       SCREAMING_SNAKE_CASE__  :					int						=np.array(
           [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]   )

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,   UpperCamelCase__       ,   atol=1e-4   )   )

import string

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			""""""
  for i in sequence:
    __magic_name__								=			ord(A_   )
    if 65 <= extract <= 90:
      output += chr(155 - extract   )
    elif 97 <= extract <= 122:
      output += chr(219 - extract   )
    else:
      output += i
  return output

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			string.ascii_letters
  __magic_name__								=			string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(A_   )] if c in letters else c for c in sequence   )

def a__       (			):






  '''simple docstring'''





  from timeit import timeit

  print("""Running performance benchmarks..."""   )
  __magic_name__								=			"""from string import printable ; from __main__ import atbash, atbash_slow"""
  print(f'''> atbash_slow(): {timeit('atbash_slow(printable)',	setup=A_   )} seconds'''   )
  print(f'''>      atbash(): {timeit('atbash(printable)',	setup=A_   )} seconds'''   )


if __name__ == "__main__":
    for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
        print(F'''{example} encrypted in atbash: {atbash(example)}''')
    benchmark()

